A new research study on equity and SMS-based personalised learning in Kenya

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Educational technologies (EdTech) have been used in a multitude of ways to enable children to gain greater access to impactful and continuous education. At the same time, differences in EdTech use and access, across and within countries, also show that the use of EdTech can contribute to exacerbating long-standing structural inequalities.

Consideration of equity is crucial in ensuring that the benefits of education are realised and technologies are used to deliver quality education to all children. However, EdTech research has tended to neglect evaluating the impact of interventions on diverse and different groups of learners. This presents a major challenge for understanding the extent to which EdTech interventions are equitable. Research has mainly focused on EdTech interventions that could be considered to be most effective in increasing children’s learning outcomes and has failed to consider heterogeneity between different groups of children. This creates equity challenges, as adequately disaggregating and designing for the end-user population has been presented as a key approach to increasing the efficacy and equity of EdTech interventions — but whether there are any differences according to gender considering intersectionality have too often simply not been addressed in previous studies.

It is within this context that EdTech Hub is launching a new research study in Kenya. The research is aligned with local priorities and realities emphasising the need for accelerating Kenyan children’s foundational learning needs. Seemingly, Kenya is marked by a high mobile phone and low internet penetration rate. The study aims to rigorously evaluate and improve the impact of an EdTech intervention on different groups of marginalised learners, with a particular focus on gender equity. This involves analysing an innovative low-tech and offline educational model that combines SMS with personalised learning technology. In this blog, we present this EdTech model and the objectives of the research study and highlight its anticipated contributions. 

In Kenya, approximately 70% of children cannot read a simple text by the age of 10, and about 29% of Grade 3 pupils cannot solve a two-step addition or subtraction. Approximately 80% of the Kenyan adult population possess a personal mobile phone, and only 43% of the adult population have access to the internet from personal devices.

Why this research?

This project is concerned with exploring the use of this educational model — that is, SMS in combination with Artificial Intelligence (AI) technology to provide personalised learning — to define how to improve children’s foundational learning in an equitable way. Personalised learning is a broad concept, covering  ‘the ways in which technology enables or supports learning, based upon particular characteristics of relevance or importance to learners.’ In the context of this project, the ‘personalisation’ reflects an adaptive system, which adjusts according to the learners’ progress and level. This type of application has been shown to ameliorate gender achievement gaps in other contexts, such as through tablet-based apps in Malawi, for example. However, the need for extra hardware such as tablets presents a barrier. By looking at a system which combines this approach with more accessible technology, this research represents a next step in the wider research agenda around personalised learning, SMS-based education, and equity. This study represents the first peer-reviewed research examining the impact of this innovative technology on learning outcomes and equity in low- and middle-income countries (LMICs). 

M-Shule model: SMS combined with personalised learning 

The implementing partner of this research is M-Shule, which means  ‘mobile school’ in Swahili. M-Shule proposes an EdTech model that combines SMS with personalised learning (using artificial intelligence) to deliver self-paced, interactive, and personalised foundational learning opportunities to offline or marginalised communities. As illustrated in the image below, this model is based on delivering SMS messages through feature and android mobile phones (with low data connectivity requirements). This will also provide tailored and personalised educational content to primary school learners in Grades 1–4 and Classes 5–8. 

The educational content and the interactive modalities of the M-Shule model have been iteratively evaluated and improved by analysing users data, refining the artificial intelligence algorithms and collaborating with various stakeholders, such as teachers, parents, and EdTech designers. M-Shule has also been engaging with the government to align the model’s learning strategies with curriculum expectations and needs. Recently, they have collaborated with high-profiles like Tusome, Education Design Unlimited (EDU) and Keep Kenya Learning, for example.

Looking ahead 

Combining SMS with personalised learning technology offers potential benefits such as being cost-effective, demonstrating positive impacts on learning and being beneficial for girls. However, the empirical evidence base for this is currently extremely limited, and further research is needed to understand these observations in practice and how they can be enhanced to further improve learning outcomes and the design of programmes. While there has been a focus during the pandemic upon SMS as an emergency measure, further research is now needed to see how the benefits of SMS can be merged with the promising evidence of personalised learning technology  and support more sophisticated pedagogical models, in order to improve children’s learning outcomes and equity beyond emergency settings. 

This research study will generate findings that are of theoretical and practical importance to researchers, practitioners, and policymakers. These partners will provide rigorous evidence to understand how to design this innovative form of EdTech to create egalitarian learning outcomes in LMICs and avoid further exacerbating structural inequalities and gender divides.

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